To solve this issue, researchers recommend using the gating mechanism to adaptively extract and learn more efficient information. Most of the time, a \(1\times 1\)  convolutional layer is adopted to accomplish the gating mechanism, which can reduce the channel dimension and leave more effective information. In SRDenseNet [1]} and MSRN [2]}, such \(1\times 1\)  convolutional layer acts as a bottleneck layer before the reconstruction module. In MemNet [3]}, it is a gate unit at the end of each memory block to control the weights of the long-term memory and short-term memory. Note that the gate is not only able to serve as bottlenecks placed at the end of the network, but also be continuously conducted in the network. For example, in MemNet [3]}, IDN[5]}, and CARN [6]}, the gating mechanism is used in both global and local region. Sometimes, it can be combined with other operations, such as attention mechanism, to construct a more effective gate module to achieve feature distillation. For instance, Li et al. proposed a hierarchical feature distillation block (Fig. REF ) by combining \(1 \times 1\)  convolutional layer and attention mechanism in MDCN [7]}.
